Identification of an array in a semiconductor specimen

ABSTRACT

There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a specimen, and more specifically, to automating theexamination of a specimen.

BACKGROUND

Current demands for high density and performance associated with ultralarge scale integration of fabricated devices require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. Such demands require formation of device features with highprecision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including automated examinationof the devices while they are still in the form of semiconductor wafers.

Examination processes are used at various steps during semiconductorfabrication to detect and classify defects on specimens. Effectivenessof examination can be increased by automatization of process(es) as, forexample, Automatic Defect Classification (ADC), Automatic Defect Review(ADR), etc.

GENERAL DESCRIPTION

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a system to examine a semiconductor specimen,the system comprising a processor and memory circuitry (PMC) configuredto obtain an image of the semiconductor specimen including one or morearrays, each including repetitive structural elements, one or moreregions, each region at least partially surrounding a correspondingarray and including features different from the repetitive structuralelements, wherein the PMC is configured to, during run-time scanning ofthe semiconductor specimen, perform a correlation analysis between pixelintensity of the image and pixel intensity of a reference imageinformative of at least one of the repetitive structural elements, toobtain a correlation matrix, use the correlation matrix to distinguishbetween one or more first areas of the image corresponding to the one ormore arrays and one or more second areas of the image corresponding theone or more regions, and output data informative of the one or morefirst areas of the image.

According to some embodiments, the system is configured to determinesub-areas of the image corresponding to values of the correlation matrixmeeting an amplitude criterion, cluster the sub-areas into one or moreclusters, based on data informative of a distance between the repetitivestructural elements in the array, and determine the one or more firstareas based at least on the one or more clusters.

According to some embodiments, the one or more arrays are separated fromthe one or more regions by one or more borders, wherein the system isconfigured to estimate the one or more first areas of the imageincluding only the at least one or more arrays up to the borders.

According to some embodiments, the system is configured to apply imageprocessing to the reference image, wherein the image processingattenuates repetitive patterns of the reference image.

According to some embodiments, the system is configured to cluster thesub-areas into one or more first clusters, based on data informative ofa distance between the repetitive structural elements in the array alonga first axis, cluster the sub-areas into one or more second clusters,based on data informative of a distance between the repetitivestructural elements in the array along a second axis, and use the firstand second clusters to distinguish between one or more first areas ofthe image corresponding to the one or more arrays and one or more secondareas of the image corresponding to the one or more regions.

According to some embodiments, the system is configured, for eachcluster, to determine a polygon surrounding one or more clusters, andoutput the polygon as a first area of the image.

According to some embodiments, the system is configured to select onlyclusters for which a number of sub-areas meets a threshold.

According to some embodiments, the system is configured to obtain datainformative of the amplitude criterion in a setup phase prior torun-time examination of the semiconductor specimen.

According to some embodiments, the system is configured to perform acorrelation analysis between pixel intensity of the one or more firstareas of the image and pixel intensity of a second reference imageinformative of at least one of the repetitive structural elements, toobtain a second correlation matrix, determine sub-areas of the one ormore first areas of the image corresponding to values of the secondcorrelation matrix meeting an amplitude criterion, determine a map ofdeformation between the one or more first areas of the image and thearray, based at least on a position of the sub-areas in the one or morefirst areas of the image and data informative of an expected position ofthe repetitive structural elements in the array, and generate acorrected image based on the map of deformation.

According to some embodiments, the system is configured to generate thecorrected image such that a position of the sub-areas in the correctedimage and data informative of an expected position of the repetitivestructural elements in the array meet a proximity criterion.

According to some embodiments, the system is configured to determinedeformation DF_(central) between a position of the sub-areas in the oneor more first areas of the image and data informative of an expectedposition of the repetitive structural elements in the array, anddetermine a map of deformation between the one or more first areas ofthe image and the array of the semiconductor specimen, based on aninterpolation method applied at least to DF_(central).

According to some embodiments, the system is configured to obtain areference image informative of at least one of the repetitive structuralelements and to select only a subset of the reference image as thesecond reference image.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method of examining a semiconductorspecimen, the method including, by a processor and memory circuitry(PMC), obtaining an image of the semiconductor specimen including one ormore arrays, each including repetitive structural elements, one or moreregions, each region at least partially surrounding a correspondingarray and including features different from the repetitive structuralelements; during run-time scanning of the semiconductor specimen,performing a correlation analysis between pixel intensity of the imageand pixel intensity of a reference image informative of at least one ofthe repetitive structural elements, to obtain a correlation matrix,using the correlation matrix to distinguish between one or more firstareas of the image corresponding to the one or more arrays and one ormore second areas of the image corresponding the one or more regions,and outputting data informative of the one or more first areas of theimage.

According to some embodiments, the method includes determining sub-areasof the image corresponding to values of the correlation matrix meetingan amplitude criterion, clustering the sub-areas into one or moreclusters, based on data informative of a distance between the repetitivestructural elements in the array, and determining the one or more firstareas based at least on the one or more clusters.

According to some embodiments, the one or more arrays are separated fromthe one or more regions by one or more borders, wherein the methodincludes estimating the one or more first areas of the image includingonly the at least one or more arrays up to the borders, and excludingthe one or more second areas corresponding to the one or more regions.

According to some embodiments, the method comprises clustering thesub-areas into one or more first clusters, based on data informative ofa distance between the repetitive structural elements in the array alonga first axis, clustering the sub-areas into one or more second clusters,based on data informative of a distance between the repetitivestructural elements in the array along a second axis, and using thefirst and second clusters to distinguish between one or more first areasof the image corresponding to the one or more arrays and one or moresecond areas of the image corresponding to the one or more regions.

According to some embodiments, the method includes selecting onlyclusters for which a number of sub-areas meets a threshold.

According to some embodiments, the method includes performing acorrelation analysis between pixel intensity of the one or more firstareas of the image and pixel intensity of a second reference imageinformative of at least one of the repetitive structural elements, toobtain a second correlation matrix, determining sub-areas of the one ormore first areas of the image corresponding to values of the secondcorrelation matrix meeting an intensity criterion, determining a map ofdeformation between the one or more first areas of the image and thearray, based at least on a position of the sub-areas in the one or morefirst areas of the image and data informative of an expected position ofthe repetitive structural elements in the array, and generating acorrected image based on the map of deformation.

According to some embodiments, the method includes determiningdeformation DF_(central) between a position of the sub-areas in the oneor more first areas of the image and data informative of an expectedposition of the repetitive structural elements in the array, anddetermining a map of deformation between the one or more first areas ofthe image and the array of the semiconductor specimen, based on aninterpolation method applied at least to DF_(central).

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a PMC, cause the PMC toperform operations as described above.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a system of examination of a semiconductorspecimen, the system comprising a processor and memory circuitry (PMC)configured to obtain an image of the semiconductor specimen includingone or more arrays, each including repetitive structural elements, oneor more regions, each region at least partially surrounding acorresponding array and including features different from the repetitivestructural elements, obtain data D_(threshold) informative of pixelintensity of at least one of the one or more arrays and the one or moreregions, wherein the PMC is configured to, during run-time scanning ofthe semiconductor specimen, determine data D_(X), D_(Y) representativeof pixel intensity along a plurality of axis in the image, use D_(X),D_(Y) and D_(threshold) to distinguish between one or more first areasof the image corresponding to the one or more arrays and one or moresecond areas of the image corresponding to the one or more regions, andoutput data informative of the one or more first areas of the image.

According to some embodiments, the system is configured to determinedata D_(X) representative of pixel intensity along each of a pluralityof lines of the image, determine data D_(Y) representative of pixelintensity along each of a plurality of columns of the image, use D_(X),D_(Y) and D_(threshold) to distinguish between one or more first areasof the image corresponding to the one or more arrays and one or moresecond areas of the image corresponding the one or more regions, andoutput data informative of the one or more first areas of the image.

According to some embodiments, each of the one or more arrays includesstructural elements which are not differentiable by visual inspection ofthe image.

According to some embodiments, the system is configured to determinedata D_(X) representative of pixel intensity along each of a pluralityof lines of the image, select a subset SL of the image including linesof the image for which D_(X) is above a first threshold, determine dataD_(Y,SL) representative of pixel intensity along each of a plurality ofcolumns of the subset SL, determine a subset C_(SL) of columns of SL forwhich D_(Y,L) is above a second threshold, determine the one or morefirst areas based at least on C_(SL).

According to some embodiments, the system is configured to determinedata D_(Y) representative of pixel intensity along each of a pluralityof columns of the image, select a subset S_(C) of the image includingcolumns of the image for which D_(Y) is above a first threshold,determine data D_(X,SC) representative of pixel intensity along each ofa plurality of lines of the subset S_(C), determine a subset L_(SC) oflines of S_(C) for which D_(X,SC) is above a second threshold, anddetermine the one or more first areas based at least on L_(SC).

According to some embodiments, the first threshold is stricter than thesecond threshold. According to some embodiments, the first threshold isstricter than the third threshold.

According to some embodiments, there is provided a corresponding method(comprising operations as described above with reference to the system)and a non-transitory computer readable medium comprising instructionsthat, when executed by a PMC, cause the PMC to corresponding operations.

According to some embodiments, the proposed solution enablesdistinguishing, in an image of a semiconductor specimen, between anarray including repetitive structural elements and a surrounding regionincluding features different from the repetitive structural elements.According to some embodiments, the proposed solution is efficient and isoperative during run-time scanning of a semiconductor specimen.According to some embodiments, a precise identification of the array isprovided, enabling extracting the array up to borders of the arrayseparating the array and the surrounding region. According to someembodiments, the proposed solution enables correction of a distortionpresent in an image of the array. In particular, efficient and precisecorrection is enabled.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a generalized block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 2 illustrates a generalized flow-chart of a method of identifyingan array including repetitive structural elements in an image of aspecimen.

FIG. 2A illustrates a non-limitative example of an image includingarrays with repetitive structural elements, and surrounding regions.

FIG. 3 illustrates a generalized flow-chart of a possible implementationof operations of the method of FIG. 2 .

FIG. 3A illustrates a non-limitative example of a correlation matrixobtained using the method of FIG. 2 .

FIG. 3B illustrates a non-limitative example of an array includingrepetitive structural elements.

FIG. 3C illustrates another non-limitative example of a correlationmatrix obtained using the method of FIG. 2 .

FIG. 4 illustrates a generalized flow-chart of a method of processing areference image used in the method of FIG. 2 .

FIGS. 4A and 4B illustrate a non-limitative example of a use of themethod of FIG. 4 .

FIG. 5 illustrates a generalized flow-chart of another method ofidentifying an array including repetitive structural elements in animage of a specimen.

FIG. 5A illustrates a non-limitative example of an application of themethod of FIG. 5 .

FIG. 6 illustrates a generalized flow-chart of another embodiment of amethod of identifying an array including repetitive structural elementsin an image of a specimen.

FIGS. 6A to 6C illustrate a non-limitative example of an application ofthe method of FIG. 6 .

FIG. 7 illustrates a non-limitative example of a distorted image of anarray including repetitive structural elements.

FIG. 8 illustrates a generalized flow-chart of a method of correctingdistortion in the image of FIG. 7 .

FIGS. 8A to 8C illustrate a non-limitative example of an application ofthe method of FIG. 8 .

FIG. 9 illustrates a generalized flow-chart of a method of processing areference image used in the method of FIG. 8 .

FIG. 9A illustrate a non-limitative example of an application of themethod of FIG. 9 .

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “obtaining”,“selecting”, “determining”, “generating”, “outputting”, “using”,“performing” or the like, refer to the action(s) and/or process(es) of acomputer that manipulate and/or transform data into other data, saiddata represented as physical, such as electronic, quantities and/or saiddata representing the physical objects. The term “computer” should beexpansively construed to cover any kind of hardware-based electronicdevice with data processing capabilities including, by way ofnon-limiting example, the system 103 and respective parts thereofdisclosed in the present application.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of wafer, masks, and other structures,combinations and/or parts thereof used for manufacturing semiconductorintegrated circuits, magnetic heads, flat panel displays, and othersemiconductor-fabricated articles.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of metrology-related operations as well asoperations related to detection and/or classification of defects in aspecimen during its fabrication. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),sampling, reviewing, measuring, classifying and/or other operationsprovided with regard to the specimen or parts thereof using the same ordifferent inspection tools. Likewise, examination can be provided priorto manufacture of the specimen to be examined and can include, forexample, generating an examination recipe(s) and/or other setupoperations. It is noted that, unless specifically stated otherwise, theterm “examination” or its derivatives used in this specification are notlimited with respect to resolution or size of an inspection area. Avariety of non-destructive examination tools includes, by way ofnon-limiting example, scanning electron microscopes, atomic forcemicroscopes, optical inspection tools, etc.

By way of non-limiting example, run-time examination can employ a twophase procedure, e.g. inspection of a specimen followed by review ofsampled locations of potential defects. During the first phase, thesurface of a specimen is inspected at high-speed and relativelylow-resolution. In the first phase, a defect map is produced to showsuspected locations on the specimen having high probability of a defect.During the second phase at least some of the suspected locations aremore thoroughly analyzed with relatively high resolution. In some cases,both phases can be implemented by the same inspection tool, and, in someother cases, these two phases are implemented by different inspectiontools.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a specimen.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g. throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats such as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format, orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter. Theexamination system 100 illustrated in FIG. 1 can be used for examinationof a specimen (e.g. of a wafer and/or parts thereof) as a part of thespecimen fabrication process. The illustrated examination system 100comprises computer-based system 103 capable of automatically determiningmetrology-related and/or defect-related information using imagesobtained during specimen fabrication. System 103 can be operativelyconnected to one or more low-resolution examination tools 101 and/or oneor more high-resolution examination tools 102 and/or other examinationtools. The examination tools are configured to capture images and/or toreview the captured image(s) and/or to enable or provide measurementsrelated to the captured image(s). System 103 can be further operativelyconnected to CAD server 110 and data repository 109.

System 103 includes a processor and memory circuitry (PMC) 104operatively connected to a hardware-based input interface 105 and to ahardware-based output interface 106. PMC 104 is configured to provideall processing necessary for operating the system 103 as furtherdetailed hereinafter (see e.g. methods described in FIGS. 2 to 5, 6, 8and 9 which can be performed at least partially by system 103) andincludes a processor (not shown separately) and a memory (not shownseparately). The processor of PMC 104 can be configured to executeseveral functional modules in accordance with computer-readableinstructions implemented on a non-transitory computer-readable memorycomprised in the PMC. Such functional modules are referred tohereinafter as comprised in the PMC. Functional modules comprised in PMC104 include a deep neural network (DNN) 112. DNN 112 is configured toenable data processing using a machine learning algorithm for outputtingapplication-related data based on the images of specimens.

By way of non-limiting example, the layers of DNN 112 can be organizedin accordance with Convolutional Neural Network (CNN) architecture,Recurrent Neural Network architecture, Recursive Neural Networksarchitecture, Generative Adversarial Network (GAN) architecture, orotherwise. Optionally, at least some of the layers can be organized in aplurality of DNN subnetworks. Each layer of the DNN can include multiplebasic computational elements (CE), typically referred to in the art asdimensions, neurons, or nodes.

Generally, computational elements of a given layer can be connected withCEs of a preceding layer and/or a subsequent layer. Each connectionbetween a CE of a preceding layer and a CE of a subsequent layer isassociated with a weighting value. A given CE can receive inputs fromCEs of a previous layer via the respective connections, each givenconnection being associated with a weighting value which can be appliedto the input of the given connection. The weighting values can determinethe relative strength of the connections and thus the relative influenceof the respective inputs on the output of the given CE. The given CE canbe configured to compute an activation value (e.g. the weighted sum ofthe inputs) and further derive an output by applying an activationfunction to the computed activation. The activation function can be, forexample, an identity function, a deterministic function (e.g., linear,sigmoid, threshold, or the like), a stochastic function, or othersuitable function. The output from the given CE can be transmitted toCEs of a subsequent layer via the respective connections. Likewise, asabove, each connection at the output of a CE can be associated with aweighting value which can be applied to the output of the CE prior tobeing received as an input of a CE of a subsequent layer. Further to theweighting values, there can be threshold values (including limitingfunctions) associated with the connections and CEs.

The weighting and/or threshold values of DNN 112 can be initiallyselected prior to training, and can be further iteratively adjusted ormodified during training to achieve an optimal set of weighting and/orthreshold values in a trained DNN. After each iteration, a difference(also called loss function) can be determined between the actual outputproduced by DNN 112 and the target output associated with the respectivetraining set of data. The difference can be referred to as an errorvalue. Training can be determined to be complete when a cost or lossfunction indicative of the error value is less than a predeterminedvalue, or when a limited change in performance between iterations isachieved. Optionally, at least some of the DNN subnetworks (if any) canbe trained separately, prior to training the entire DNN.

System 103 is configured to receive, via input interface 105, inputdata. Input data can include data (and/or derivatives thereof and/ormetadata associated therewith) produced by the examination tools and/ordata produced and/or stored in one or more data repositories 109 and/orin CAD server 110 and/or another relevant data depository. It is notedthat input data can include images (e.g. captured images, images derivedfrom the captured images, simulated images, synthetic images, etc.) andassociated numeric data (e.g. metadata, hand-crafted attributes, etc.).It is further noted that image data can include data related to a layerof interest and/or to one or more other layers of the specimen.

System 103 is further configured to process at least part of thereceived input data and send, via output interface 106, the results (orpart thereof) to a storage system 107, to examination tool(s), to acomputer-based graphical user interface (GUI) 108 for rendering theresults and/or to external systems (e.g. Yield Management System (YMS)of a FAB). GUI 108 can be further configured to enable user-specifiedinputs related to operating system 103.

By way of non-limiting example, a specimen can be examined by one ormore low-resolution examination machines 101 (e.g. an optical inspectionsystem, low-resolution SEM, etc.). The resulting data (referred tohereinafter as low-resolution image data 121), informative oflow-resolution images of the specimen, can be transmitted—directly orvia one or more intermediate systems—to system 103. Alternatively oradditionally, the specimen can be examined by a high-resolution machine102 (e.g. a subset of potential defect locations selected for review canbe reviewed by a scanning electron microscope (SEM) or Atomic ForceMicroscopy (AFM)). The resulting data (referred to hereinafter ashigh-resolution image data 122) informative of high-resolution images ofthe specimen can be transmitted—directly or via one or more intermediatesystems—to system 103.

It is noted that images of a desired location on a specimen can becaptured at different resolutions. By way of non-limiting example,so-called “defect images” of the desired location are usable todistinguish between a defect and a false alarm, while so-called “classimages” of the desired location are obtained with higher resolution andare usable for defect classification. In some embodiments, images of thesame location (with the same or different resolutions) can compriseseveral images registered therebetween (e.g. images captured from thegiven location and one or more reference images corresponding to thegiven location).

It is noted that image data can be received and processed together withmetadata (e.g. pixel size, text description of defect type, parametersof image capturing process, etc.) associated therewith.

Upon processing the input data (e.g. low-resolution image data and/orhigh-resolution image data, optionally together with other data as, forexample, design data, synthetic data, etc.), system 103 can send theresults (e.g. instruction-related data 123 and/or 124) to any of theexamination tool(s), store the results (e.g. defect attributes, defectclassification, etc.) in storage system 107, render the results via GUI108 and/or send to an external system (e.g. to YMS).

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1 ; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and/or hardware.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools can be implemented as inspectionmachines of various types, such as optical imaging machines, electronbeam inspection machines and so on. In some cases, the same examinationtool can provide low-resolution image data and high-resolution imagedata. In some cases, at least one examination tool can have metrologycapabilities.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in other embodiments atleast some examination tools 101 and/or 102, data repositories 109,storage system 107 and/or GUI 108 can be external to the examinationsystem 100 and operate in data communication with system 103 via inputinterface 105 and output interface 106. System 103 can be implemented asstand-alone computer(s) to be used in conjunction with the examinationtools. Alternatively, the respective functions of the system can, atleast partly, be integrated with one or more examination tools.

Attention is now drawn to FIG. 2 . A method includes obtaining(operation 200) an image 250 of a specimen. According to someembodiments, image 250 is acquired by an examination tool (such asexamination tool 101) during run-time scanning of the specimen. Thespecimen includes one or more arrays 260. The arrays 260 includerepetitive structural elements (represented as reference 261 in one ofthe arrays). The repetitive structural elements include e.g. memorycells (such as SRAM, DRAM, FRAM, Flash memory), programmable logiccells, etc. These examples are not limitative. Generally, the repetitivestructural elements are arranged in each array according to a repetitivepattern or grid. For example, distance between two adjacent repetitivestructural elements (according to a horizontal axis and a vertical axis)is constant or at least substantially constant among the various arrays.

The specimen includes one or more regions 265. Each region 265 surroundsat least partially a corresponding array 260. The region 265 does notinclude the repetitive structural elements which are present in thearray 260. In the non-limitative example of FIG. 2A, the specimenincludes vertical and horizontal regions 265 surrounding the arrays 260.The regions 265 can correspond e.g. to stiches. Each region 265 includesfeatures different from the repetitive structural elements 260. In someembodiments, the regions 265 can include non-repetitive features, and/orrepetitive features which are different from the repetitive structuralelements 260. Examples of non-repetitive features include e.g. logics.This is however not limitative.

The method further includes performing (operation 210) a correlationanalysis between pixel intensity of the image 250 and pixel intensity ofa reference image informative of at least one of the repetitivestructural elements. The reference image can include e.g. an image ofone of the repetitive structural elements. The reference image is alsocalled a “golden cell”. According to some embodiments, the referenceimage is generated based on design data. According to some embodiments,the reference image is obtained from an image of a structural elementwhich is known (e.g. from previous analysis) to be free of defects.According to some embodiments, the reference image is obtained during asetup phase, prior to run-time examination of the specimen. In the setupphase, time and processing constraints are less strict and therefore itis possible to acquire an image of one of the repetitive structuralelements which will constitute the reference image.

An output of the correlation analysis performed at 210 can include acorrelation matrix, which includes a plurality of values. Each value isassociated to a sub-area of the image 250 and indicates the level ofcorrelation between pixel intensity in the sub-area and pixel intensityin the reference image.

The method can further include using (operation 220) the correlationmatrix to distinguish between one or more first areas of the image 250corresponding to the one or more arrays 260 and one or more second areasof the image corresponding to the one or more regions 265. FIG. 2Aillustrates examples of the one or more first areas 270 and of the oneor more second areas 275. In some embodiments, all areas of the imagewhich have not been identified as belonging to the one or more firstareas 270 are considered to be part of the one or more second areas 275.

The method further includes outputting 230 data informative of the oneor more first areas 270 of the image 250. This can include e.g.outputting location of the one or more first areas 270 in the image 250,and/or outputting a selection of the image 250 including only the one ormore first areas 270. According to some embodiments, the method caninclude outputting location of the one or more second areas 275, and/oroutputting a selection of the image 250 including only the one or moresecond areas 275.

According to some embodiments, at least operation 210, 220 and 230 areperformed during run-time scanning of the specimen. In other words, themethod of identification of the arrays in the image is efficient andtherefore can be performed during a run-time phase.

According to some embodiments, identification of the one or more firstareas 270 in the image is used during run-time scanning of the specimen,e.g. by a PMC configured to determine data representative of defects inthe array (e.g. location of the defects, class of the defects, etc.). Inparticular, the PMC can implement an algorithm for detecting defects,which is specifically tailored to detect defects in an array includingrepetitive structural elements.

As shown in FIG. 2A, the one or more arrays 260 are separated from theone or more regions 265 by one or more borders 266. The border 266defines a physical limit between an array 260 and a correspondingsurrounding region 265.

According to some embodiments, the method enables estimating the one ormore first areas 270 of the image 250 including only the at least one ormore arrays 260 up to the borders 266. In particular, according to someembodiments, the method enables identifying the arrays 260 up to theborders 266, excluding the one or more second areas 275 corresponding tothe one or more regions 265.

Attention is drawn to FIG. 3 which depicts a non-limitativeimplementation of operations 210 to 230.

As explained with reference to operation 210, a correlation matrix isobtained. A non-limitative example of a correlation matrix 365 obtainedfor a given region of the image 250 is depicted in FIG. 3A.

The method can therefore include determining (operation 300) sub-areasof the image 250 corresponding to values of the correlation matrix 365meeting an amplitude criterion. The amplitude criterion can e.g. definethat sub-areas of the image 250 for which a local maximal correlationpeak is identified (in some embodiments an absolute threshold can beset) correspond to location of the repetitive structural elements in theimage 250. According to some embodiments, during a setup phase prior torun-time examination of the specimen, a first estimation of theamplitude of the correlation peak which is obtained for a sub-areaincluding one of the repetitive structural elements is obtained, whichcan be used to determine the amplitude criterion used during run-timeexamination and for which it is considered that a structural element ispresent.

As shown in FIG. 3A, the correlation matrix 365 includes peaks ofcorrelation 367 (maximal values), located at sub-areas 375 of the image.These sub-areas 375 correspond to an estimation of the location of therepetitive structural elements. Indeed, since the correlation analysisinvolves correlating pixel intensity of the image with pixel intensityof a reference image informative of a repetitive structural element, itis expected that sub-areas of the image, including the repetitivestructural elements, will provide a high correlation value relative tosub-areas of the image (regions 265) which do not include the repetitivestructural elements.

The method can further include (operation 310) clustering the sub-areas375 into one or more clusters, based on data informative of a distancebetween the repetitive structural elements in the array.

As mentioned above, the repetitive structural elements are generallyarranged according to a repetitive pattern or grid. Therefore, it ispossible to obtain a distance between two consecutive structuralelements in an array. In some embodiments, data (see reference 368 inFIG. 3B) informative of a distance between the repetitive structuralelements 361 in the array along a first axis (e.g. horizontal axis,corresponding to lines of the image) and data (see reference 369 in FIG.3B) informative of a distance between the repetitive structural elementsin the array along a second axis (e.g. vertical axis, corresponding tocolumns in the image) can be obtained. As illustrated, the distance canbe evaluated between centres of the structural elements.

According to some embodiments, operation 310 can include clustering thesub-areas 375 into one or more first clusters, based on data informativeof a distance between the repetitive structural elements in the arrayalong a first axis 372. According to some embodiments, in a givencluster, any sub-area 375 is located from another sub-area 375 of thecluster at a distance below or equal to the distance between therepetitive structural elements along the first axis. A non-limitativeexample is illustrated in FIG. 3A, in which sub-areas are assigned tothe same cluster 370 along the first axis 372. As shown, sub-areas 374are not assigned to the cluster 370 since the distance from eachsub-area to the cluster 370 is above the distance between two repetitivestructural elements along the first axis 372.

According to some embodiments, operation 310 can include clustering thesub-areas into one or more second clusters, based on data informative ofa distance between the repetitive structural elements in the array alonga second axis 373. According to some embodiments, in a given cluster,any sub-area is located from another sub-area of the cluster at adistance below or equal to the distance between the repetitivestructural elements along the second axis 373. A non-limitative exampleis illustrated in FIG. 3A, in which sub-areas are assigned to the samecluster 381 along the second axis 373. As shown, sub-area 383 is notassigned to the cluster 381 since the distance the sub-area 383 to thecluster 381 is above the distance between two repetitive structuralelements along the second axis 373.

The method includes determining (operation 320) the one or more firstareas based at least on the one or more clusters. In particular, thefirst clusters can be used to determine size and location of the one ormore first areas along the first axis 372 and the second clusters can beused to determine size and location of the one or more first areas alongthe second axis 373. For example, cluster 370 provides size and locationof a first area along axis 372 and cluster 381, which intersects cluster370, provides size and location of the same first area along axis 373.As a consequence, a first area 384 is identified. This can be performedfor all clusters, which are used to determine limits of the differentfirst areas.

According to some embodiments, another operation is performed toidentify first areas using the clusters. In particular, the method caninclude determining a polygon (e.g. a rectangle, or a square)surrounding one or more clusters identified as defining a first area,and outputting the polygon as the first area. For example, in theexample of FIG. 3A, a rectangle 392 can be generated which covers afirst area identified based on the first cluster 370 and the secondcluster 381.

Attention is now drawn to FIG. 3C. According to some embodiments, themethod can include selecting (operation 330) only clusters for which anumber of sub-areas present in the cluster meets a threshold (e.g. isabove the threshold). This is illustrated in FIG. 3C. A plurality ofclusters 383 ₁ to 383 ₅ have been identified along axis 372. A cluster,referred to as 383 ₁, includes only one sub-area 384. Since it is knownthat the array includes repetitive structural elements arranged along arepetitive pattern (e.g. a grid), it can be assumed that the sub-area384 does not correspond to a structural element, because the repetitivepattern does not include isolated structural elements. Therefore, thiscluster can be ignored or deleted when determining the one or more firstareas at operation 320. The same can be applied to clusters determinedalong the second axis 373 (second clusters): if a given cluster includesa number of sub-areas which are below a threshold, the given cluster isignored when determining the one or more first areas at operation 320.

Attention is now drawn to FIG. 4 . According to some embodiments, thereference image informative of at least one of the repetitive structuralelements can be processed using an image processing algorithm. Accordingto some embodiments, the image processing algorithm attenuatesrepetitive patterns of the reference image. For example, partialwhitening can be applied to the reference image. Partial whitening caninclude e.g. transforming the reference image in the frequency domain(e.g. transforming X(i,j) indicative of the pixels of the referenceimage to X(f) in the frequency domain), degrading high/strongfrequencies (e.g.

$\left( {{e.g.{X^{\prime}(f)}} = \frac{X(f)}{\sqrt{❘{X(f)}❘}}} \right)$and performing an inverse transformation to revert to an image (e.g.transforming X′(f) into X′(i,j)).

A non-limitative example of the method of FIG. 4 is depicted in FIG. 4A.As shown, the array 460 includes repetitive structural elements 410 andconductive lines 411. The conductive lines 411 extend up to the region465 surrounding the array 460. As shown in FIG. 4B, a reference image470 informative of the repetitive structural elements 410 has beenobtained. Image processing (e.g. partial whitening), which attenuatesrepetitive patterns, is applied to the reference image 470 in order toobtain a corrected reference image 475. As shown, both the conductivelines 411 (which correspond to repetitive patterns) and the structuralelements 410 (which also correspond to repetitive patterns) areattenuated in the corrected reference image 475. As a consequence, whena correlation is performed between the corrected reference image 475 andthe image (as explained with reference to operation 210), the sub-areascorresponding to the structural elements will provide a highercorrelation value than the sub-areas corresponding to the region,although both the sub-areas and the region include, in this embodiment,common repetitive features (conductive lines 411), thereby facilitatingdistinguishing between the array and the surrounding region.

According to some embodiments, a method can include obtaining (operation500) an image of a specimen including one or more arrays and one or moresurrounding regions. Operation 500 is similar to operation 200 above. Insome embodiments, the image is acquired by an electron beam examinationtool. In some cases, the image can have low signal to noise ratio, andtherefore, the method of FIG. 2 which involves correlation with areference image, is not always applicable. Low signal to noise ratio canbe due to the size of the features present in the specimen, chargingeffects, etc. In some embodiments, due to the low signal to noise ratio,the structural elements of the arrays cannot be identified/distinguishedwithin the arrays by visual inspection of the image. In someembodiments, the size of a pixel in the image can be larger than a sizeof a structural element and therefore the structural elements cannot bedistinguished by visual inspection.

The method further includes obtaining (operation 510) data D_(threshold)informative of pixel intensity of at least one of an array and of asurrounding region. D_(threshold) can be obtained in particular during asetup phase, prior to run-time examination of the specimen. For example,during a setup phase, an image of a specimen similar to the specimenunder examination during run-time is obtained. An operator, or anautomatic algorithm (e.g. K-means algorithm) provides a first estimationof the location of the arrays and of the surrounding regions within theimage. An average value P_(array) of the pixel intensity of the arraysis computed and an average value P_(region) of the pixel intensity ofthe surrounding regions is computed. These two values are expected to bedifferent since the arrays and the surrounding regions contain differentstructural features. D_(threshold) can be computed based e.g. onP_(array) and on P_(region). D_(threshold) can correspond e.g. to theaverage between these two values, but this is not limitative.

The method further includes determining (operation 520) datarepresentative of pixel intensity along a plurality of axis in theimage. This can include in particular determining data D_(X)representative of pixel intensity along each of a plurality of lines ofthe image, and data D_(Y) representative of pixel intensity along eachof a plurality of columns of the image. Data D_(X) (resp. D_(Y)) can becomputed e.g. as an average value of pixel intensity along each line(resp. column) of the image.

The method further includes using (operation 530) D_(X), D_(Y) andD_(threshold) to distinguish between one or more first areas of theimage corresponding to the one or more arrays and one or more secondareas of the image corresponding the one or more regions.

Operation 530 can include identifying lines of the image for which D_(X)is above (or below, depending on whether the pixel intensity is higherfor arrays or for the surrounding regions) the threshold D_(threshold)(obtained e.g. during a setup phase), and columns of the image for whichD_(Y) is above the threshold D_(threshold) (obtained e.g. during a setupphase). Intersection of the lines and the columns which have beenidentified provides identification of the location of the arrays.

A non-limitative example is provided in FIG. 5A. Assume for example thatduring a setup phase, it has been determined that pixel intensity is (onaverage) higher for arrays than for the surrounding regions (P_(array)is larger than P_(region)), and that D_(threshold) has been set as anaverage value of P_(array) and P_(region). Data D_(X) informative ofpixel intensity along the lines is depicted as curve 545 (this curve ispurely illustrative and not limitative). As shown, for lines of theimage on which the arrays 562 are located, the curve is above thethreshold D_(threshold) (referred to as 548). Data D_(Y) informative ofpixel intensity along the columns is depicted as curve 561 (this curveis purely illustrative and not limitative). As shown, for columns of theimage on which the arrays 562 are located, the curve is above thethreshold D_(threshold) (referred to as 548).

The method further includes outputting (operation 540) data informativeof the one or more first areas of the image. Operation 540 is similar tooperation 230 described above. In particular, according to someembodiments, intersection between lines for which curve 545 is above thethreshold 548, and columns for which curve 561 is above the threshold549 provides an estimation of the location of the first areascorresponding to the location of the arrays.

According to some embodiments, at least operations 510, 530 and 540 areperformed during run-time scanning of the specimen. In other words, themethod of identification of the arrays in the image is efficient andtherefore can be performed during a run-time phase.

Attention is now drawn to FIG. 6 . In some cases, data representative ofpixel intensity along lines and/or columns of the image on which thearrays are located can be close to data representative of pixelintensity along other lines and/or columns. A non-limitative example isillustrated in FIG. 6A, in which an array 660 is located at the bottomleft corner of the image and is surrounded by a large region 665. Themethod of FIG. 6 is a possible embodiment of a solution which enablesimproving differentiation between lines and columns of the image onwhich the arrays are located, and other lines and columns.

The method includes determining (operation 610) data D_(X)representative of pixel intensity (e.g. average of pixel intensity alongthe lines) along each of a plurality of lines of the images. D_(X) isrepresented as curve 668 in FIG. 6A. Assume a first thresholdD_(threshold,1) has been obtained (e.g. during setup phase beforerun-time examination). D_(threshold,1) is informative of pixel intensityof at least one of the arrays and the regions. In some embodiments,D_(threshold,1) can be selected as a strict threshold (high threshold)to maximize probability of distinguishing between lines of the imagecorresponding to the arrays and other lines. For example, assume thatduring a setup phase (performed on an image of a specimen similar to thespecimen under examination during run-time), an average value P_(array)of the pixel intensity of the arrays has been computed and an averagevalue P_(region) of the pixel intensity of the surrounding regions hasbeen computed (as explained above). Assume for example that P_(array) ishigher than P_(region). D_(threshold,1) can selected with a value whichis higher than P_(region) to maximize probability to remove linescorresponding to the surrounding regions. For example, D_(threshold,1)can be selected as follows: D_(threshold,1)=P_(region)+N*σ (with σ thestandard deviation of the pixel intensity of the surrounding regions,and N is an integer equal e.g. to 2).

The method includes selecting a subset SL (represented as 682) of theimage including lines of the image for which D_(X) is aboveD_(threshold,1). As shown, subset SL includes lines 683 of the image onwhich the array 660 is located, and additional lines 684 of the imagewhich do not include the array 660 (however, the pixel intensity ofthese additional lines is above D_(threshold,1)). The method furtherincludes determining (operation 630) data D_(Y,SL) (curve 686 in FIG.6B) representative of pixel intensity (e.g. average of pixel intensityalong the columns) along each of a plurality of columns of the subsetSL. The method includes determining (operation 640) a subset C_(SL)(referred to as 689) of columns of SL for which D_(Y,SL) is above asecond threshold 690. This second threshold 690 can be obtained based onmeasurements performed during a setup phase, prior to run-timeexamination. For example, the second threshold can be set equal to anaverage value of P_(array) (average pixel intensity of the arrays) andP_(region) (average pixel intensity of the surrounding regions). This ishowever not limitative.

Columns C_(SL) (reference 689) indicate position of the array along theline axis. Determination of position and size of the array along thecolumn axis (Y axis) of the image can then be performed which thusyields the one or more first areas (operation 650), corresponding to thearray(s) in the image. Position of the one or more first areas(corresponding to the array(s)) in the image can be provided and/orposition of one or more second areas (corresponding to the surroundingregions) in the image (which correspond to all areas which have not beenidentified as the first areas) can be provided.

Indeed, once the columns 689 of the image corresponding to the arrayhave been identified, it is then easier to distinguish between the linesof the image including the array, and the other lines of the image, asvisible in FIG. 6C. A subset S′_(L) (referred to as 692 in FIG. 6C) ofthe image is considered. This subset S′_(L) includes all lines of theimage, and is limited to the columns 689 of the image identified in theprevious operation. The method further includes determining data(referred to as 693) representative of pixel intensity (e.g. averagepixel intensity) along each of a plurality of lines of the subset S′_(L)(692), and determining lines 694 of the image for which data 693 isabove a third threshold 695 (in some embodiments, the third threshold695 is equal to the second threshold 690, but this is not mandatory). Asvisible in FIG. 6C, it is now easier to differentiate between lines ofthe image including the array and other lines based on pixel intensity.These lines 694, together with the columns 689, define the one or morefirst areas of the image corresponding to the array(s). The other areasof the image correspond to second areas of the image corresponding tothe region(s) surrounding the array(s).

In the example of FIGS. 6 to 6C, the method starts by selecting a subsetSL of the image including lines for which average pixel intensity isabove a threshold. It is understood that the method can be performedequivalently by first selecting a subset of columns. In this case, themethod can include:

-   -   determining data D_(Y) representative of pixel intensity along        each of a plurality of columns of the image (equivalent to        operation 610);    -   selecting a subset S_(C) of the image including columns of the        image for which D_(Y) is above a first threshold (equivalent to        operation 620);    -   determining data D_(X,SC) representative of pixel intensity        along each of a plurality of lines of the subset S_(C)        (equivalent to operation 630);    -   determining a subset L_(SC) of lines of S_(C) for which D_(X,SC)        is above a second threshold (equivalent to operation 640);    -   determining the one or more first areas corresponding to the        array(s) based at least on L_(SC) (equivalent to operation        650—since the lines of the image corresponding to the array are        known, it becomes easier to identify the columns of the image        corresponding to the array, similarly to what was explained with        reference to FIG. 6C).

According to some embodiments, this method also enables estimating theone or more first areas of the image including only the at least one ormore arrays up to the borders separating the array(s) from thesurrounding region(s). In particular, according to some embodiments, themethod enables identifying the arrays up to the borders, excluding theone or more second areas corresponding to the one or more regions.

Attention is now drawn to FIG. 7 . Assume that an image of a specimenhas been obtained, in which one or more first areas corresponding to oneor more arrays 710 each including repetitive structural elements 720have been identified. This identification can rely e.g. on the variousembodiments described above, or can rely on other identificationmethods. Therefore, an image 700 limited to the one or more first areas(corresponding to the arrays) is available, without the region(s)surrounding the one or more arrays. In some embodiments, the image 700includes both one or more first areas (corresponding to the arrays), andone or more second areas (corresponding to the regions). Since positionof the first areas is known, it is possible to operate only on the firstareas. It will be referred hereinafter to an image 700 including onlythe first areas (corresponding to the array(s)), but it is understoodthat the method can be applied similarly to an image including bothfirst and second areas, by applying the method only on the first areasof the image.

As visible in FIG. 7 , in some embodiments, image 700 of the array(s) isdistorted. In particular, position of the structural elements 720 in thearray as visible in the image 700 is not compliant with their expectedposition (true position in the specimen) in the array. This can be dueto various factors, such as measurement errors of the examination tool,etc.

Distortion can be problematic when attempting to use image 700 forvarious applications, such as defect detection and/or classification. Itis therefore required to correct this distortion. FIG. 8 illustrates anembodiment of a method of correcting the distortion present in the imageof the array(s).

The method includes performing (operation 800) a correlation analysisbetween pixel intensity of the image 700 and pixel intensity of areference image informative of at least one of the repetitive structuralelements. The reference image used at operation 800 can be differentfrom the reference image used at operation 210 to identify the firstareas of the image corresponding to the array (in this case, a secondreference image is used at operation 800, different from a firstreference image used at operation 210). This is, however, not mandatory.An output of the correlation analysis is a second correlation matrix(which can be different from the correlation matrix obtained atoperation 210). In some embodiments, it is possible to reuse thecorrelation matrix obtained at operation 210 (in this case, only thevalues corresponding to the one or more first areas are used).

The method can further include determining (operation 810) sub-areas ofthe image corresponding to values of the second correlation matrixmeeting an amplitude criterion. In particular, the amplitude criterioncan dictate that sub-areas of the image, associated with maximal values(e.g. local maximal values) of the second correlation matrix, areidentified.

As shown in FIG. 8A, the second correlation matrix 860 includes peaks ofcorrelation (maximal values) located at given sub-areas 685. Thesesub-areas 865 correspond to an estimation of the location of therepetitive structural elements (in particular to a central area of eachstructural element). Indeed, since the correlation analysis involvescorrelating pixel intensity of the image with pixel intensity of areference image informative of a repetitive structural element, it isexpected that sub-areas of the image 700, including the repetitivestructural elements, will provide a high correlation value relative tosub-areas of the image 700 which do not include the repetitivestructural elements.

The method can further include determining (operation 820) a map ofdeformation between the image 700 and the array. The map of deformationcan be determined based on a position of the sub-areas (as determinedusing the second correlation matrix) and on data informative of anexpected position of the repetitive structural elements in the array.

A non-limitative example is depicted in FIG. 8B, which illustratesposition of the sub-areas 865 corresponding to maximal values of thesecond correlation matrix, and expected position 866 of the structuralelements in the array. For each sub-area, it is possible to determine avector of deformation 867, indicative of the difference between theposition of the structural element in the image (estimated using themaximal values of the second correlation matrix) and the expectedposition 866 of the corresponding structural element.

According to some embodiments, a map of deformation can be determinedfor the whole image. Indeed, as mentioned above, a deformation (see 867,hereinafter “DF_(central)”) between a position of the sub-areas 865 inthe image and data informative of an expected position of the repetitivestructural elements in the array is determined. This corresponds to thedeformation of the central part of each of the structural element withrespect to its expected position. In order to determine deformation ofother pixels of the image (which do not necessarily correspond to thecentral part of the structural element), the method can include applyingan interpolation method on the values of DF_(central) over the image.This provides an estimation of the deformation for all other pixelslocated between the different sub-areas 865. According to someembodiments, the interpolation method is applied separately for thedeformation along the X axis (lines of the image) and for thedeformation along the Y axis (columns of the image).

The method can further include generating (operation 830) a correctedimage 880 (see FIG. 8C) based on the map of deformation. This caninclude moving the pixels of the image based on the map of deformation,so that a position of the sub-areas 865 (corresponding to the peaks ofthe second correlation matrix) in the corrected image 880 and datainformative of an expected position of the repetitive structuralelements in the array, meet a proximity criterion (e.g. the differencein position is below a threshold).

Attention is now drawn to FIG. 9 . According to some embodiments, amethod can include obtaining (900) a reference image informative of atleast one of the repetitive structural elements and selecting (910) onlya subset of the reference image as the second reference image. Accordingto some embodiments, size of the subset is based on a compromise. On onehand, size of the subset must be large enough to be able to identify theposition of the structural elements on the image, and on the other hand,size of the subset must be small enough to obtain a sufficient number ofcorrelation values.

A non-limitative example is illustrated in FIG. 9A.

A reference image 920 has been obtained. A subset 930 of the referenceimage 920 is selected. This subset can be used as the second referenceimage in the method of FIG. 8 .

According to some embodiments, the subset 930 can be selected using aniterative method, during e.g. a setup phase. The method starts with afirst subset (maximal size of this subset can be set e.g. by a user).The method of FIG. 8 is performed using this first subset. Then, theresolution is increased, meaning that the size of the first subset isdecreased. The method of FIG. 8 is performed again using this newsubset, and performance of the output is compared to the previousiteration. If the performance is improved, then the method is repeatedwith a new subset of smaller size. If the performance is not improved,then the method is stopped and the subset obtained at the previousiteration is selected.

It is to be understood that the invention is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings.

It will also be understood that the system according to the inventionmay be, at least partly, implemented on a suitably programmed computer.Likewise, the invention contemplates a computer program being readableby a computer for executing the method of the invention. The inventionfurther contemplates a non-transitory computer-readable memory tangiblyembodying a program of instructions executable by the computer forexecuting the method of the invention.

The invention is capable of other embodiments and of being practiced andcarried out in various ways. Hence, it is to be understood that thephraseology and terminology employed herein are for the purpose ofdescription and should not be regarded as limiting. As such, thoseskilled in the art will appreciate that the conception upon which thisdisclosure is based may readily be utilized as a basis for designingother structures, methods, and systems for carrying out the severalpurposes of the presently disclosed subject matter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention as hereinbefore described without departing from its scope,defined in and by the appended claims.

What is claimed is:
 1. A system to examine a semiconductor specimen, thesystem comprising a processor and memory circuitry (PMC) configured to:obtain an image of the semiconductor specimen including: one or morearrays, each including repetitive structural elements; and one or moreregions, each region at least partially surrounding a correspondingarray and including features different from the repetitive structuralelements; and wherein the PMC is configured to, during run-time scanningof the semiconductor specimen: perform a correlation analysis betweenpixel intensity of the image and pixel intensity of a reference imageinformative of at least one of the repetitive structural elements, toobtain a correlation matrix, determine sub-areas of the imagecorresponding to values of the correlation matrix meeting an amplitudecriterion, cluster the sub-areas into one or more clusters, based ondata informative of a distance between the repetitive structuralelements in the array, determine one or more first areas based at leaston the one or more clusters, use the correlation matrix to distinguishbetween the one or more first areas of the image corresponding to theone or more arrays and one or more second areas of the imagecorresponding the one or more regions, and output data informative ofthe one or more first areas of the image.
 2. The system of claim 1,wherein the one or more arrays are separated from the one or moreregions by one or more borders, wherein the PMC is further configured toestimate the one or more first areas of the image including only atleast one or more arrays up to the borders.
 3. The system of claim 1,the PMC further configured to apply image processing to the referenceimage, wherein the image processing attenuates repetitive patterns ofthe reference image.
 4. The system of claim 1, the PMC furtherconfigured to: cluster the sub-areas into one or more first clusters,based on data informative of a distance between the repetitivestructural elements in the array along a first axis, cluster thesub-areas into one or more second clusters, based on data informative ofa distance between the repetitive structural elements in the array alonga second axis, and use the first and second clusters to distinguishbetween the one or more first areas of the image corresponding to theone or more arrays and the one or more second areas of the imagecorresponding to the one or more regions.
 5. The system of claim 1, thePMC further configured, for each cluster, to: determine a polygonsurrounding the one or more clusters, and output the polygon as a firstarea of the image.
 6. The system of claim 1, wherein the one or moreclusters include only clusters for which a number of sub-areas meets athreshold.
 7. The system of claim 1, the PMC further configured toobtain data informative of the amplitude criterion in a setup phaseprior to run-time examination of the semiconductor specimen.
 8. Thesystem of claim 1, the PMC further configured to: perform a correlationanalysis between pixel intensity of the one or more first areas of theimage and pixel intensity of a second reference image informative of atleast one of the repetitive structural elements, to obtain a secondcorrelation matrix, determine sub-areas of the one or more first areasof the image corresponding to values of the second correlation matrixmeeting an amplitude criterion, determine a map of deformation betweenthe one or more first areas of the image and the array, based at leaston a position of the sub-areas in the one or more first areas of theimage and data informative of an expected position of the repetitivestructural elements in the array, and generate a corrected image basedon the map of deformation.
 9. The system of claim 8, the PMC furtherconfigured to generate the corrected image such that a position of thesub-areas in the corrected image and data informative of an expectedposition of the repetitive structural elements in the array meet aproximity criterion.
 10. The system of claim 8, the PMC furtherconfigured to: determine deformation DF_(central) between a position ofthe sub-areas in the one or more first areas of the image and datainformative of an expected position of the repetitive structuralelements in the array, and determine a map of deformation between theone or more first areas of the image and the array of the semiconductorspecimen, based on an interpolation method applied at least toDF_(central).
 11. The system of claim 8, the PMC further configured toobtain the reference image informative of at least one of the repetitivestructural elements and to select only a subset of the reference imageas the second reference image.
 12. A method of examining a semiconductorspecimen by a processor and memory circuitry (PMC), the methodcomprising: obtaining an image of the semiconductor specimen including:one or more arrays, each including repetitive structural elements; andone or more regions, each region at least partially surrounding acorresponding array and including features different from the repetitivestructural elements; and during run-time scanning of the semiconductorspecimen: performing a correlation analysis between pixel intensity ofthe image and pixel intensity of a reference image informative of atleast one of the repetitive structural elements, to obtain a correlationmatrix, determining sub-areas of the image corresponding to values ofthe correlation matrix meeting an amplitude criterion, clustering thesub-areas into one or more clusters, based on data informative of adistance between the repetitive structural elements in the array,determining one or more first areas based at least on the one or moreclusters, using the correlation matrix to distinguish between the one ormore first areas of the image corresponding to the one or more arraysand one or more second areas of the image corresponding the one or moreregions, and outputting data informative of the one or more first areasof the image.
 13. The method of claim 12, wherein the one or more arraysare separated from the one or more regions by one or more borders,wherein the method further comprises estimating the one or more firstareas of the image including only at least one or more arrays up to theborders, and excluding the one or more second areas corresponding to theone or more regions.
 14. The method of claim 12, further comprising:clustering the sub-areas into one or more first clusters, based on datainformative of a distance between the repetitive structural elements inthe array along a first axis, clustering the sub-areas into one or moresecond clusters, based on data informative of a distance between therepetitive structural elements in the array along a second axis, andusing the first and second clusters to distinguish between one or morefirst areas of the image corresponding to the one or more arrays and oneor more second areas of the image corresponding to the one or moreregions.
 15. The method of claim 12, wherein the one or more clustersinclude only clusters for which a number of sub-areas meets a threshold.16. The method of claim 12, further comprising: performing a correlationanalysis between pixel intensity of the one or more first areas of theimage and pixel intensity of a second reference image informative of atleast one of the repetitive structural elements, to obtain a secondcorrelation matrix, determining sub-areas of the one or more first areasof the image corresponding to values of the second correlation matrixmeeting an intensity criterion, determining a map of deformation betweenthe one or more first areas of the image and the array, based at leaston a position of the sub-areas in the one or more first areas of theimage and data informative of an expected position of the repetitivestructural elements in the array, and generating a corrected image basedon the map of deformation.
 17. The method of claim 16, furthercomprising: determining deformation DF_(central) between a position ofthe sub-areas in the one or more first areas of the image and datainformative of an expected position of the repetitive structuralelements in the array, and determining a map of deformation between theone or more first areas of the image and the array of the semiconductorspecimen, based on an interpolation method applied at least toDF_(central).
 18. A non-transitory computer readable medium comprisinginstructions that, when executed by a PMC, cause the PMC to performoperations comprising: obtaining an image of a semiconductor specimenincluding: one or more arrays, each including repetitive structuralelements; and one or more regions, each region at least partiallysurrounding a corresponding array and including features different fromthe repetitive structural elements; and during run-time scanning of thesemiconductor specimen: performing a correlation analysis between pixelintensity of the image and pixel intensity of a reference imageinformative of at least one of the repetitive structural elements, toobtain a correlation matrix, determining sub-areas of the imagecorresponding to values of the correlation matrix meeting an amplitudecriterion, clustering the sub-areas into one or more clusters, based ondata informative of a distance between the repetitive structuralelements in the array, determining one or more first areas based atleast on the one or more clusters, using the correlation matrix todistinguish between the one or more first areas of the imagecorresponding to the one or more arrays and one or more second areas ofthe image corresponding the one or more regions, and outputting datainformative of the one or more first areas of the image.